The present disclosure relates generally to systems and methods for monitoring electrophysiologic information from a subject or patient, and, in particular, to systems and methods for removing artifacts from electrophysiologic information generated in relation to magnetic resonance imaging (“MRI”) systems.
Electrophysiologic recordings, such as electroencephalography (“EEG”), electrocardiography (“ECG”) or galvanic skin response (“GSR”), conducted in an MRI scanner offer a useful means to enhance the utility of MRI measurements. In the specific instance of brain function assessment, EEG recordings supply brain activity information with a high temporal resolution. When combined with high spatial resolution, soft tissue resolution and biochemical versatility of MRI measurements, as provided by functional magnetic resonance imaging with blood oxygen level-dependent (“fMRI-BOLD”) contrast or arterial spin labeling (“ASL”), synchronous examination of brain function across spatial and temporal scales may be achieved. Correlating haemodynamic changes in brain with concomitant electrophysiologic signatures offers enhanced potential to monitor brain function in normal, clinical and pathological states. For example, the goal of identifying brain regions generating a known electrophysiologic signature, such as an epileptic discharge, necessitates resolving both millisecond-scale electrical signatures (only measurable with EEG) with millimeter-scale brain areas with increased activity (only measurable on fMRI) concomitantly. This kind of brain function assessment is known as electroencephalography-correlated fMRI (“EEG-fMRI”). Generally, additional electrophysiologic signals are also acquired during MRI scanning, which include respiratory or cardiac function measurements, thus providing complementary information to the MRI assessment of physiologic parameters under study.
The utility of these techniques, however, is fundamentally limited by the presence of magnetic fields and gradients in the MRI scanner, along with radiofrequency (“RF”) signals transmitted and received during imaging. These features of the MR-environment introduce artifacts in electrophysiologic recordings conducted within the MR scanner. As an example of MR environment related artifacts in electrophysiologic recordings, ballistocardiogram (“BCG”) artifacts are induced in brain EEG recordings on account of motion of head and scalp electrodes, primarily due to cardiac and blood flow pulsatile movements, within the magnetic field of the scanner. In particular, BCG artifacts have significantly larger amplitudes (150-200 microVolts at 1.5 Tesla) than underlying EEG activity (10-100 microVolts), and so EEG activity can be obscured up to 20 Hz, lowering specificity and sensitivity of the EEG recordings acquired in proximity to an MRI scanner. Moreover, additional complications arise from the fact that changes in heart rate, blood pressure and resulting pulsatile head motion cause variations in the shape, timing and intensity of the BCG artifacts, making predictability and removal of BCG artifacts very challenging.
Several attempts to remove BCG artifacts from EEGs have been previously reported. For example, a common approach includes measurement of a reference signal obtained from electrocardiograms (“ECG”) or motion sensors. The reference signal is used to generate a waveform template that defines an estimate for BCG artifacts, which is then subtracted from contaminated EEG measurements to produce corrected EEG signals. However, this approach relies on high quality ECG or motion data in order to robustly perform peak detection and/or adaptive filtering, which becomes particularly difficult in magnetic fields greater than 1.5 Tesla or during long EEG recordings, since reference signals acquired in a MRI scanner are also often corrupted.
In addition, some reference signal-free BCG removal methods, such as independent component analysis and wavelet basis decompositions, have also been explored. These approaches rely on the separability between true EEG signals and BCG artifacts with respect to signal amplitude, time and/or frequency. However, many basis elements often contain substantial overlap between signals and artifacts in these domains. This skews the separation, and necessitates subjective and case-specific criteria to define which basis elements may be excluded as “artifact” and which ones may be retained as “true EEG signal.” In addition, such criteria demand significant post-algorithmic-processing.
The above limitations make current methods inadequate for providing the quality of measurements required for monitoring or investigating brain function for cognitive studies and clinical applications. Thus, there is a need for systems and methods that do not require reference signals or subjective separation criteria for removing artifacts from electrophysiologic measurements acquired in a MRI scanner.